Open Source Intelligence Initiating Efficient Investigation and Reliable Web Searching

Tiwari, Shiva; Verma, Ravi; Jaiswal, Janvi; Kumar, B. Vinoth

doi:10.1007/978-981-15-6634-9_15

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The model works by following an algorithm Global Search (GS) that further divides into four sub-algorithms that are listed as follows [21]: the internet, usually for Web indexing purposes [22] (see Fig. 7).…”

Section: Methodsmentioning

confidence: 99%

Using Open Source Intelligence as a Tool for Reliable Web Searching

Kumar¹,

Verma²,

Tiwari³

2021

SN COMPUT. SCI.

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Open source intelligence (OSINT) refers to the gathering and retrieval of data and information from online servers or sources that are freely accessible or are open-source. OSINT has been prominent over decades, under one name or the other. With the advent of instant communication and rapid knowledge exchange, much actionable and informative knowledge can now be accessed from unclassified, public sources. OSINT can help to collect data from the web and use it according to a model's requirements and needs. A web search tool has been proposed here to look for similar advantages, explaining the tool's functionalities and development strategy, which aims to minimize target search time and use minimum input data for the same. In addition, to provide a different means for searching and data filtering so that only the best information is provided.

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Section: Methodsmentioning

confidence: 99%

Using Open Source Intelligence as a Tool for Reliable Web Searching

Kumar¹,

Verma²,

Tiwari³

2021

SN COMPUT. SCI.

Self Cite

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A systematic review on research utilising artificial intelligence for open source intelligence (OSINT) applications

Browne,

Abedin,

Chowdhury

2024

Int. J. Inf. Secur.

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This paper presents a systematic review to identify research combining artificial intelligence (AI) algorithms with Open source intelligence (OSINT) applications and practices. Currently, there is a lack of compilation of these approaches in the research domain and similar systematic reviews do not include research that post dates the year 2019. This systematic review attempts to fill this gap by identifying recent research. The review used the preferred reporting items for systematic reviews and meta-analyses and identified 163 research articles focusing on OSINT applications leveraging AI algorithms. This systematic review outlines several research questions concerning meta-analysis of the included research and seeks to identify research limitations and future directions in this area. The review identifies that research gaps exist in the following areas: Incorporation of pre-existing OSINT tools with AI, the creation of AI-based OSINT models that apply to penetration testing, underutilisation of alternate data sources and the incorporation of dissemination functionality. The review additionally identifies future research directions in AI-based OSINT research in the following areas: Multi-lingual support, incorporation of additional data sources, improved model robustness against data poisoning, integration with live applications, real-world use, the addition of alert generation for dissemination purposes and incorporation of algorithms for use in planning.

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